A popular approach to train the discrete neurons is the REINFORCE-like [1]} algorithms. It is an unbiased estimator but often with high gradient variance [2]}. The original HM-RNN applied straight-through estimator [3]} because of its efficiency and simplicity in practice. In this paper, instead, we applied a more recent Gumbel-sigmoid [4]} [5]} to estimate the stochastic neurons. It is much more efficient than other approaches and achieved state-of-the-art performance among many other gradient estimators [4]}.
